The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations. In this work, we propose a novel approach called \gansan whose objective is to prevent the possibility of \emph{any} discrimination i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our sanitization algorithm \gansan is partially inspired by the powerful framework of generative adversarial networks (in particuler the Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible and thus preserving the interpretability of the sanitized data. As a consequence, once the sanitizer is trained, it can be applied to new data, such as for instance, locally by an individual on his profile before releasing it. Finally, experiments on a real dataset demonstrate the effectiveness of the proposed approach as well as the achievable trade-off between fairness and utility.

More and more organizations are beginning to use or expand their use of artificial intelligence (AI) tools and services in the workplace. Despite AI's proven potential for enhancing efficiency and decision-making, it has raised a host of issues in the workplace which, in turn, have prompted an array of federal and state regulatory efforts that are likely to increase in the near future. Artificial intelligence, defined very simply, involves machines performing tasks in a way that is intelligent. The AI field involves a number of subfields or forms of AI that solve complex problems associated with human intelligence--for example, machine learning (computers using data to make predictions), natural-language processing (computers processing and understanding a natural human language like English), and computer vision or image recognition (computers processing, identifying, and categorizing images based on their content). One area where AI is becoming increasingly prevalent is in talent acquisition and recruiting.

I just briefly wanted to say a little bit about my background. I studied Math and Computer Science in college and then did a Ph.D. in Math. I worked as a quant in Energy Trading and that's where I first started working with data. I was an early data scientist and backend developer at Uber. I taught full stack software development at Hackbright. I really love teaching and I think I'll always return to teaching in some form. And then two years ago, together with Jeremy Howard, I started fast.ai with the goal of making deep learning more accessible and easier to use. I'm on Twitter @math_rachel and, as William said, I blog about diversity on Medium @racheltho, and I blog about data science at fast.ai. I just have one slide about fast.ai. We have this, as William mentioned, a totally free course, "Practical Deep Learning for Coders." The only prerequisite is one year of coding experience. It's distinctive in that there are no advanced math prerequisites, yet it takes you to the state-of-the-art. We've had a lot of success. We've had students get jobs at Google Brain, have their work featured on HBO and in Forbes, launch new companies, get new jobs.

Siri and Alexa are good examples of AI, as they listen to human speech, recognize words, perform searches, and translate the text results back into speech. A recent purchase of an AI company called Dynamic Yield by McDonald's -- which analyzes customer spending/eating habits and recommend them other food to purchase -- has taken the use of AI to the next step. AI technologies raise important issues like personal privacy rights and whether machines can ever make fair decisions. There are two main areas where regulation can be helpful. One good example of this is the use of AI to read lips.

San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. SAN FRANCISCO – A routine traffic stop goes dangerously awry when a police officer's body camera uses its built-in facial recognition software to misidentify a motorist as a convicted felon. At best, lawsuits are launched. That imaginary scenario is what some California lawmakers are trying to avoid by supporting Assembly Bill 1215, the Body Camera Accountability Act, which would ban the use of facial recognition software in police body cams – a national first if it passes a Senate vote this summer and is signed by Gov. Gavin Newsom. State law enforcement officials here do not now employ the technology to scan those in the line of sight of officers.

San Francisco District Attorney George Gascon, left, announces a new AI tool that will curb racial biases when deciding criminal charges, alongside Alex Chohlas-Wood, right, who helped develop the tool.ASSOCIATED PRESS San Francisco says it will start using an artificial intelligence tool to reduce possible racial bias among prosecutors reviewing police reports, a "first-in-the-nation" use of a technology whose applications have been criticized for compounding bias. On Wednesday, District Attorney George Gascón announced that the city on July 1 would begin to use a "bias mitigation tool" that automatically redacts anything on the police report that might be suggestive of race, from hair color to zip code. Information about the police officer, such as badge number, will also be hidden. Currently, the district attorney's office manually removes the first few pages of the report, but if any race details are in the narrative--the section where the police officer describes the crime--prosecutors can see them. "This technology will reduce the threat that implicit bias poses to the purity of decisions which have serious ramifications for the accused, and that will help make our system of justice more fair and just," Gascón said.

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AI Ethics is now a global topic of discussion in academic and policy circles. At least 63 public-private initiatives have produced statements describing high-level principles, values, and other tenets to guide the ethical development, deployment, and governance of AI. According to recent meta-analyses, AI Ethics has seemingly converged on a set of principles that closely resemble the four classic principles of medical ethics. Despite the initial credibility granted to a principled approach to AI Ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance. Significant differences exist between medicine and AI development that suggest a principled approach in the latter may not enjoy success comparable to the former. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional accountability mechanisms. These differences suggest we should not yet celebrate consensus around high-level principles that hide deep political and normative disagreement.

Despite having a $12 billion budget and being located adjacent to Silicon Valley, San Francisco doesn't always take advantage of the ways in which tech can improve civic life or the work of its city employees. But there is one office that is pushing the envelope and collaborating with programmers, nonprofits, and computer scientists with the vital goal of improving its criminal justice practices. Just last month District Attorney Geroge Gascón announced that a partnership with Code for America had enabled his office to clear all old marijuana convictions made defunct with the passage of Proposition 64. And on Wednesday, he shared the news that a new collaboration with Stanford was in the works, to employ artificial intelligence as a means of mitigating implicit racial bias among his staff. If the words "artificial intelligence" combined with "criminal justice system" give you goosebumps, you're not alone.

Deloitte Global announced that Deloitte firms are now offering clients an artificial intelligence (AI) platform. The platform can monitor, measure, and analyze changes in tax regulation in real-time to give Deloitte clients the edge in monitoring and responding to regulatory updates across the world. The AI platform is the result of a collaboration between Deloitte firms and Signal A.I. Signal's proprietary AI technology was trained by Deloitte tax experts to understand key regulatory changes in over 100 jurisdictions from over 100 regulators, tax authorities and government bodies. "Tax professionals are being asked to do more with fewer resources--to stay ahead of more risks, draw insights from more data, track more regulations across more jurisdictions. To address these challenges, they are transforming how they operate and manage processes by implementing new technologies, such as AI, robotic process automation and natural language processing," says Conrad Young, Deloitte Global Tax & Legal, Chief Digital Officer.